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Comparative Analysis Tumor Detection and Segmentation Exploitation Watershed Technique

S. Josephine1 , S. Murugan2

Section:Survey Paper, Product Type: Journal Paper
Volume-06 , Issue-11 , Page no. 221-227, Dec-2018

Online published on Dec 31, 2018

Copyright © S. Josephine, S. Murugan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S. Josephine, S. Murugan, “Comparative Analysis Tumor Detection and Segmentation Exploitation Watershed Technique,” International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.221-227, 2018.

MLA Style Citation: S. Josephine, S. Murugan "Comparative Analysis Tumor Detection and Segmentation Exploitation Watershed Technique." International Journal of Computer Sciences and Engineering 06.11 (2018): 221-227.

APA Style Citation: S. Josephine, S. Murugan, (2018). Comparative Analysis Tumor Detection and Segmentation Exploitation Watershed Technique. International Journal of Computer Sciences and Engineering, 06(11), 221-227.

BibTex Style Citation:
@article{Josephine_2018,
author = {S. Josephine, S. Murugan},
title = {Comparative Analysis Tumor Detection and Segmentation Exploitation Watershed Technique},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2018},
volume = {06},
Issue = {11},
month = {12},
year = {2018},
issn = {2347-2693},
pages = {221-227},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=575},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=575
TI - Comparative Analysis Tumor Detection and Segmentation Exploitation Watershed Technique
T2 - International Journal of Computer Sciences and Engineering
AU - S. Josephine, S. Murugan
PY - 2018
DA - 2018/12/31
PB - IJCSE, Indore, INDIA
SP - 221-227
IS - 11
VL - 06
SN - 2347-2693
ER -

           

Abstract

Five totally different threshold segmentation primarily based approaches are reviewed and compared up here to extract the growth from set of brain pictures. This analysis focuses on the analysis of image segmentation ways, a comparison of 5 semi-automated ways are undertaken for evaluating their relative performance within the segmentation of growth. Consequently, results square measure compared on the idea of quantitative and analysis of individual ways. The aim of this study was to analytically determine the ways, most fitted for application for a selected genre of issues. The results show that of the region growing segmentation performed on top of rest in most cases.

Key-Words / Index Term

Tumor, MRI, Region Growing, Segmentation, Watershed, FCM

References

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